1. Field of the Invention
This invention relates generally to prognostics, and more particularly to methods for processing in-situ monitored sensor data for health assessment and remaining life predictions of products and systems.
2. Description of the Related Art
There has been a growing interest in monitoring the ongoing “health” of products and systems. Here, health assessment includes evaluation of the extent of degradation or deviation from an expected normal condition. Prognostics is the process of predicting the time to failure of a part or system based on assessment of health conditions. Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated with respect to the actual life-cycle conditions, to predict the advent of failure, and thus to mitigate system risks.
The PHM methods process uses sensor data and other signals, and product environmental and operational information to extract parameters that can be used to meet several important applications such as to provide an early warning of failure, to forecast maintenance requirements as needed: avoid scheduled maintenance and extend maintenance cycles, to assess the potential for life extensions, to reduce amount of redundancy, to provide guidance for system re-configuration and self-healing, to provide efficient fault detection and identification, including evidence of “failed” equipment found to function properly when re-tested (no-fault found), and to improve future designs and qualification methods. Other applications in product screening, qualification and warranty assessment are also possible.
Implementation of prognostics and health assessment techniques involves monitoring and processing of environmental and operational loads, and performance parameters to assess the health of the product. Typical environmental loads can include temperature, vibrations, shock, pressure, acoustic levels, strain, stress, inert environments, humidity levels, and contamination. Operational loads include usage frequency, usage severity, duty cycle, power, heat dissipation, current, voltage, and mechanical loads such as force, torsion, pressure etc. Performance parameters are the measure of the product's or system's performance and can include power, efficiency, voltage, resistance, RF signal strength, throughput or any parameters specific to the product or system under consideration.
Life cycle environmental and operational loads, both individually or in various combinations, may lead to performance or physical degradation of the product and subsequently reduce its service life. The extent and rate of product degradation depends upon the product and the nature, magnitude, and duration of exposure to these loads. The damage inflicted, and hence the “life” of the product consumed can be assessed by monitoring and processing the load and performance data in real time, and correlating it with governing failure models, such as physics-of-failure based stress and damage models.
The data can be monitored using sensors embedded in the product or systems or autonomous sensor systems retro-fitted to the systems. Other data may be obtained from operation and performance conditions. The processing of data can be achieved by various methods including, 1) onboard processing in real time, 2) transferring data to external (base-station) databases or centralized servers, and 3) using intermediate processing on sensor nodes embedded with processing capabilities to enable transmitting fewer amounts of data (processed instead of raw data) to a base station.
Data simplification is a way to obtain gains in computing speed and testing time, condense load histories without sacrificing important damage characteristics, preserve the interaction of load parameters, and provide an estimate of the error introduced by reducing and simplifying the data. Data simplification can be achieved using a variety of tools such as filters, Fourier transforms, wavelets, Hayes method, ordered overall range (OOR), etc.
Besides, data simplification it is often necessary to process the “raw” data (e.g. from sensors) to make it compatible with the damage models and algorithms needed to conduct prognostics. In particular, it may be necessary to extract relevant load parameters. Load parameters (single or multiple) measure for example, the magnitude and/or intensity of a load. To illustrate, in the case of vibration loading, the frequency of vibration, and vibration g-forces would be specific load parameters. Other examples include cyclic mean, amplitudes, ramp rates, hold periods, etc. Methods used to extract load parameters from a given set of load data/signals are referred to as load parameter extraction methods. Commonly used load parameter extraction methods include: cycle counting algorithms for extracting cycles from time-load signal, Fast Fourier transforms (FFT) for extracting the frequency content of signals, etc. Depending on the application and type of signal, custom load extraction methods may be required.
FIG. 1 is an exemplary schematic of in-situ monitoring, pre-processing, and storage of environmental and usage loads. A time (t)-temperature (T) signal is monitored in-situ using sensors, and further processed to extract (in this case) cyclic temperature range (ΔT), cyclic mean temperature (Tmean), ramp rate (dT/dt), and dwell time (tD) using embedded load extraction algorithms. The extracted load parameters are then stored in appropriate bins to achieve further data reduction. The binned data is downloaded to estimate the distributions of the load parameters for use in damage assessment, remaining life estimation, and the accumulation of product usage history.
The applications and limitations of existing methods for extracting load-time parameters will now be described, including the Hayes method, Ordered Overall Range (OOR), Peak counting, and Rainflow counting. Hayes' method identifies small ranges which are interruptions of a larger range. An interruption is found for a peak-valley pair when the next peak is higher than the current peak. An interruption is found for a valley-peak pair when the next valley is lower than the current valley. Once damage is calculated for these cycles, they are screened out of the original block of data, producing the abbreviated blocks. The procedure is repeated to cover all blocks.
The OOR method (also called the Racetrack method) converts irregular data into sequences of peaks and valleys by eliminating small reversals using a screening level. Peaks and valleys that were originally separated by smaller interrupting ranges now become adjacent, creating larger overall ranges.
Peak Counting records relative maxima and minima in the load history and their load levels. Generally only peaks and valleys above and below preset reference levels are counted. Similar to level crossing, the most damaging cycle is recorded between the largest peak and valley. In Rainflow Counting two consecutive ranges are considered together. Based on a set of rules, the algorithm scans the entire time-load history to identify full cycle and half cycles. The Rainflow method provides the mean stress in addition to the stress range during counting.
In terms of prognostic assessment, a method which includes calculating an accumulated damage estimate for a component via a diagnostics function is reported by Bonanni, et al., U.S. Pat. No. 7,328,128. Greis, et al., U.S. Pat. No. 7,333,917 reports a novelty detection system that may determine whether the novel state is indicative of normal operation or of a potential abnormal operation. A neural network based model has been proposed by Harrison, et al., U.S. Pat. No. 7,277,823 Several other ideas have been reported on performing prognostics on specific systems such as machinery (Crowder, Jr., U.S. Pat. No. 6,748,341) and gear-box and rotating equipment (Husseiny, U.S. Pat. No. 5,210,704). Other ideas utilize special sensor hardware such as thin piezoelectric sensors (Giurgiutiu, U.S. Pat. No. 7,024,315).
Limitations of Existing Methods
None of the methods noted above provide a generic method to analyze combined operational, environmental, and performance data to provide the prognostic assessment. The specific limitations with existing load extraction methods for application in prognostics and health assessment include the failure to extract ramp rates and dwell time, the need for smart data reduction and filtering techniques, and the need for assessing correlation of load parameters, as is discussed below.
a. Extracting Ramp Rates and Dwell Information
The existing load extraction methods provide the load range and mean load. These parameters may be adequate parameters for single fatigue life estimation in elastic-plastic fatigue analysis of materials. However, for example, in case of thermal-fatigue loads (frequently observed in electronic systems), wherein the damage is characterized by plastic yielding and creep deformations, the estimation of dwell time and ramp rates is required in addition to the stress range and mean stress for accurate fatigue assessment. Ramp rates can be estimated with modifications in the Rainflow algorithm. However, extracting dwell times and corresponding load levels is more challenging since it depends on both the amplitude and ramp rate of the monitored load cycles.
b. Concerns with Data Reduction
Analysis of complex load histories typically involves a data reduction stage to enable efficient data processing and to eliminate undesired load reversals. However, data reduction methods may also eliminate important load information and omit the extraction of certain load parameters in subsequent stages. For example the Ordered Overall Range (OOR) can eliminate small cycles (as a fraction of large cycles) by choosing an appropriate value of S-parameter (S<1). However, in the process it also eliminates dwell-time information. In FIG. 2, the points and connecting lines in light shade depict the original data before reduction. The dark solid line connecting the end points depicts the results obtained after OOR. The dwell time load information (0.2 hours of at 60° C.) is critical to assess the damage due to creep mechanisms at various locations on the electronic assembly such as solder joints, plated through holes, and die-attaches. Yet, this information is discarded in the analysis process.
c. Correlation of Load Parameters
Distributions of in-situ monitored temperature load parameters can be randomly sampled and used with the damage model in a Monte Carlo simulation. For accurate damage assessment the correlation between load parameters is important. Quantifying and using the correlations for damage assessment, enables the generation of realistic scenarios during random sampling. For example, the distributions of measured ΔT and Tmean can be used for assessing the solder joint damage due to cyclic thermal loading. However, it is essential to know the correlation between these two parameters, as cycles with small ΔT but higher Tmean values will cause more damage than the cycles with same ΔT values but lower Tmean.